Semantic Caching for LLM Applications: A Review on Reducing Latency and Costs

Semantic caching for AI query optimization aims to address challenges associated with high latency and computational costs in Large Language Model (LLM) applications. By leveraging semantic caching and efficient query optimization techniques, significant improvements can be realized in terms of reduced system load, enhanced response times, and optimized resource utilization. This review delves into the principles of semantic caching and its applications in LLM query optimization, focusing on various architectures and implementation methodologies. The review further provides a comparative analysis of existing caching models, summarizing the state-of-the-art approaches designed to achieve optimal latency reduction and cost efficiency in LLM applications.